A single-sensor digital camera generally employs a color filter array (CFA) in order to capture full-color information from a single two dimensional array of light-sensitive pixels. The CFA comprises an array of color filters that filter the light being detected by each pixel. As a result, each pixel receives light from only one color, or in the case of a panchromatic or “clear” filter, from all colors. In order to reproduce a full-color image from the CFA image, three color values must be produced at each pixel location. This is accomplished by interpolating the missing color values from neighboring pixel values. This interpolation process is often referred to as CFA interpolation or demosaicing.
Prior to CFA interpolation, the image data exists in a sparse dataset representation, i.e., only one color value per pixel. It is computationally advantageous to noise-clean, or denoise, the image data in this representation rather than after CFA interpolation when there will be three or more color values per pixel to be processed. Many approaches to denoising CFA image data are found in the prior art.
A number of well-known methods for denoising full-color images can also be applied to CFA images with appropriate adjustments. One such method is anisotropic diffusion, a type of partial differential equation (PDE) denoising, as first described by Perona et al. in the article “Scale-space and edge detection using anisotropic diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, pp. 629-639, 1990. Perona et al. only discuss denoising full-resolution grayscale (luminance) images and do not teach the denoising of full-color or CFA images.
Tschumperlé et al., in the article “Diffusion PDEs on vector-valued images”, IEEE Signal Processing Magazine, Vol. 19, Issue 5, pp. 16-25, September 2002, expand on the method of Perona et al. to include the denoising of full-color images, but no mention is made of CFA images.
Many approaches simply treat each color within the CFA image separately, resulting in independent grayscale (luminance) denoising operations. Examples are found in U.S. Pat. No. 6,229,578, to Acharya et al., entitled “Edge-detection based noise removal algorithm,” U.S. Pat. No. 6,625,325 to Gindele et al., entitled “Noise cleaning and interpolating sparsely populated color digital image using a variable noise cleaning kernel,” U.S. Pat. No. 7,369,165, to Bosco et al., entitled “Noise filter for Bayer pattern image data,” U.S. Pat. No. 7,418,130 to Keshet et al., entitled “Edge-sensitive denoising and color interpolation of digital images,” and U.S. Patent Application 2009/0219417, to Tsuruokam entitled “Image capturing system and computer readable recording medium for recording image processing program.” All these approaches suffer from the inability to directly reduce chrominance noise in the CFA image.
Other approaches address reducing both luminance and chrominance noise explicitly in CFA images. Combinations of different channels that exist at the same location are also called channels. The chrominance channels, either explicitly or implicitly are of the form of red minus green (R-G) and blue minus green (B-G), or red minus luminance (R-Y), and blue minus luminance (B-Y). Examples of this approach are given in U.S. Patent Application 2006/0152596 to Adams Jr. et al., entitled “Noise cleaning sparsely populated color digital images,” and U.S. Patent Application 2009/0052797 to Matsushita et al., entitled “Imaging device, image processing device, image processing method, program for image processing method, and recording medium having program for image processing method recorded thereon.” The problem with these approaches is that while chrominance values such as R-G are easy to compute, they are generally not devoid of luminance information, such as edge and text detail. This, in turn, reduces the ability to denoise chrominance information without degrading luminance information in the image. A better luminance-chrominance transform is described in U.S. Pat. No. 5,644,358 to Miyano et al., entitled “Automatic white balance adjusting device.” This transform provides an improved separation of luminance and chrominance information while still being easy to compute.
Thus, there exists a need for a means of denoising luminance and chrominance information in a CFA image without requiring explicit or implied demosaicking operations.